

import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__A			:					Dict						=			'src/transformers'
__A			:					List[Any]						=			'docs/source/en'
__A			:					str						=			'.'





def 	__a     (      A__			:  List[Any]			,  A__			:  int			,  A__			:  Dict       ):





    with open(A_			,  "r"			,  encoding="utf-8"			,  newline="\n"       ) as f:
        SCREAMING_SNAKE_CASE										=   f.readlines()
    # Find the start prompt.
    SCREAMING_SNAKE_CASE										=   0
    while not lines[start_index].startswith(A_       ):
        start_index += 1
    start_index += 1

    SCREAMING_SNAKE_CASE										=   start_index
    while not lines[end_index].startswith(A_       ):
        end_index += 1
    end_index -= 1

    while len(lines[start_index]       ) <= 1:
        start_index += 1
    while len(lines[end_index]       ) <= 1:
        end_index -= 1
    end_index += 1
    return "".join(lines[start_index:end_index]       ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__A			:					str						=			'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__A			:					List[str]						=			re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A			:					Any						=			re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A			:					Optional[int]						=			re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
__A			:					Union[str, Any]						=			direct_transformers_import(TRANSFORMERS_PATH)





def 	__a     (      A__			:  List[str]       ):
    SCREAMING_SNAKE_CASE										=   re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"			,  A_       )
    return [m.group(0       ) for m in matches]





def 	__a     (      A__			:  List[str]			,  A__			:  int       ):
    SCREAMING_SNAKE_CASE										=   2 if text == "✅" or text == "❌" else len(A_       )
    SCREAMING_SNAKE_CASE										=   (width - text_length) // 2
    SCREAMING_SNAKE_CASE										=   width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    SCREAMING_SNAKE_CASE										=   {
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    SCREAMING_SNAKE_CASE										=   {name: config.replace("Config"			,  ""       ) for name, config in model_name_to_config.items()}

    # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
    SCREAMING_SNAKE_CASE										=   collections.defaultdict(A_       )
    SCREAMING_SNAKE_CASE										=   collections.defaultdict(A_       )
    SCREAMING_SNAKE_CASE										=   collections.defaultdict(A_       )
    SCREAMING_SNAKE_CASE										=   collections.defaultdict(A_       )
    SCREAMING_SNAKE_CASE										=   collections.defaultdict(A_       )

    # Let's lookup through all transformers object (once).
    for attr_name in dir(A_       ):
        SCREAMING_SNAKE_CASE										=   None
        if attr_name.endswith("Tokenizer"       ):
            SCREAMING_SNAKE_CASE										=   slow_tokenizers
            SCREAMING_SNAKE_CASE										=   attr_name[:-9]
        elif attr_name.endswith("TokenizerFast"       ):
            SCREAMING_SNAKE_CASE										=   fast_tokenizers
            SCREAMING_SNAKE_CASE										=   attr_name[:-13]
        elif _re_tf_models.match(A_       ) is not None:
            SCREAMING_SNAKE_CASE										=   tf_models
            SCREAMING_SNAKE_CASE										=   _re_tf_models.match(A_       ).groups()[0]
        elif _re_flax_models.match(A_       ) is not None:
            SCREAMING_SNAKE_CASE										=   flax_models
            SCREAMING_SNAKE_CASE										=   _re_flax_models.match(A_       ).groups()[0]
        elif _re_pt_models.match(A_       ) is not None:
            SCREAMING_SNAKE_CASE										=   pt_models
            SCREAMING_SNAKE_CASE										=   _re_pt_models.match(A_       ).groups()[0]

        if lookup_dict is not None:
            while len(A_       ) > 0:
                if attr_name in model_name_to_prefix.values():
                    SCREAMING_SNAKE_CASE										=   True
                    break
                # Try again after removing the last word in the name
                SCREAMING_SNAKE_CASE										=   "".join(camel_case_split(A_       )[:-1]       )

        # Let's build that table!
    SCREAMING_SNAKE_CASE										=   list(model_name_to_config.keys()       )
    model_names.sort(key=str.lower       )
    SCREAMING_SNAKE_CASE										=   ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
    # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
    SCREAMING_SNAKE_CASE										=   [len(A_       ) + 2 for c in columns]
    SCREAMING_SNAKE_CASE										=   max([len(A_       ) for name in model_names]       ) + 2

    # Build the table per se
    SCREAMING_SNAKE_CASE										=   "|" + "|".join([_center_text(A_			,  A_       ) for c, w in zip(A_			,  A_       )]       ) + "|\n"
    # Use ":-----:" format to center-aligned table cell texts
    table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]       ) + "|\n"

    SCREAMING_SNAKE_CASE										=   {True: "✅", False: "❌"}
    for name in model_names:
        SCREAMING_SNAKE_CASE										=   model_name_to_prefix[name]
        SCREAMING_SNAKE_CASE										=   [
            name,
            check[slow_tokenizers[prefix]],
            check[fast_tokenizers[prefix]],
            check[pt_models[prefix]],
            check[tf_models[prefix]],
            check[flax_models[prefix]],
        ]
        table += "|" + "|".join([_center_text(A_			,  A_       ) for l, w in zip(A_			,  A_       )]       ) + "|\n"
    return table





def 	__a     (      A__			:  Any=False       ):
    SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   _find_text_in_file(
        filename=os.path.join(A_			,  "index.md"       )			,  start_prompt="<!--This table is updated automatically from the auto modules"			,  end_prompt="<!-- End table-->"			,  )
    SCREAMING_SNAKE_CASE										=   get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(A_			,  "index.md"       )			,  "w"			,  encoding="utf-8"			,  newline="\n"       ) as f:
                f.writelines(lines[:start_index] + [new_table] + lines[end_index:]       )
        else:
            raise ValueError(
                "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."       )


if __name__ == "__main__":
    __A			:					Tuple						=			argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    __A			:					Optional[Any]						=			parser.parse_args()

    check_model_table(args.fix_and_overwrite)

import math

def a__       (			A_   ):






  '''simple docstring'''





  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5,	int(math.sqrt(A_   ) + 1   ),	6   ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True

def a__       (			A_ = 0.1   ):






  '''simple docstring'''





  __magic_name__								=			3
  __magic_name__								=			3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1,	(j + 2) * (j + 2),	j + 1   ):
      primes += is_prime(A_   )
    j += 2
  return j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

